The algorithm is initialized with complete image bounding box proposal and
alternates between above steps until convergence.
We use highly efficient GPU
solver in [1]} for SVM optimization.
Finally, test proposal
\(\mathbf {x}\)  from the test set \(\mathcal {D}_{\rm test}\)  is scored using the SVM trained for
each class.
To have a fair comparison, we use the same Faster-RCNN model trained
on the base classes as we used in our model to extract bounding box and feature
proposals from all images.
For the objectness model \(O\) , we learn a
class-agnostic logistic regression model on the extracted feature.
We employ the hard negative mining in [2]} to improve the
performance of the classifier.
In this approach the negative set is initialized
with full negative image features and the hardest negative proposal within each
image is added to the negative set after each re-training step.
